OpenRaiser/Intern-Atlas huggingface dataset:
Towards A Sign Language Gloss Representation Of Modern Standard Arabic
Venue: arXiv cs.CL 2020
Year: 2020

Over 5% of the world's population (466 million people) has disabling hearing loss. 4 million are children. They can be hard of hearing or deaf. Deaf people mostly have profound hearing loss. Which implies very little or no hearing. Over the world, deaf people often communicate using a sign language with gestures of both hands and facial expressions. The sign language is a full-fledged natural language with its own grammar and lexicon. Therefore, there is a need for translation models from and to sign languages. In this work, we are interested in the translation of Modern Standard Arabic(MSAr) into sign language. We generated a gloss representation from MSAr that extracts the features mandatory for the generation of animation signs. Our approach locates the most pertinent features that maintain the meaning of the input Arabic sentence.
Dim Silicon and the Case for Improved DVFS Policies
Venue: arXiv cs.OS 2020
Year: 2020

Due to thermal and power supply limits, modern Intel CPUs reduce their frequency when AVX2 and AVX-512 instructions are executed. As the CPUs wait for 670μs before increasing the frequency again, the performance of some heterogeneous workloads is reduced. In this paper, we describe parallels between this situation and dynamic power management as well as between the policy implemented by these CPUs and fixed-timeout device shutdown policies. We show that the policy implemented by Intel CPUs is not optimal and describe potential better policies. In particular, we present a mechanism to classify applications based on their likeliness to cause frequency reduction. Our approach takes either the resulting classification information or information provided by the application and generates hints for the DVFS policy. We show that faster frequency changes based on these hints are able to improve performance for a web server using the OpenSSL library.
Compose Like Humans: Jointly Improving the Coherence and Novelty for Modern Chinese Poetry Generation
Venue: arXiv cs.CL 2020
Year: 2020

Chinese poetry is an important part of worldwide culture, and classical and modern sub-branches are quite different. The former is a unique genre and has strict constraints, while the latter is very flexible in length, optional to have rhymes, and similar to modern poetry in other languages. Thus, it requires more to control the coherence and improve the novelty. In this paper, we propose a generate-retrieve-then-refine paradigm to jointly improve the coherence and novelty. In the first stage, a draft is generated given keywords (i.e., topics) only. The second stage produces a "refining vector" from retrieval lines. At last, we take into consideration both the draft and the "refining vector" to generate a new poem. The draft provides future sentence-level information for a line to be generated. Meanwhile, the "refining vector" points out the direction of refinement based on impressive words detection mechanism which can learn good patterns from references and then create new ones via insertion operation. Experimental results on a collected large-scale modern Chinese poetry dataset show that our proposed approach can not only generate more coherent poems, but also improve the diversity and novelty.
Continuous and monotone machines
Venue: arXiv cs.LO 2020
Year: 2020

We investigate a variant of the fuel-based approach to modeling diverging computation in type theories and use it to abstractly capture the essence of oracle Turing machines. The resulting objects we call continuous machines. We prove that it is possible to translate back and forth between such machines and names in the standard function encoding used in computable analysis. Put differently, among the operators on Baire space, exactly the partial continuous ones are implementable by continuous machines and the data that such a machine provides is a description of the operator as a sequentially realizable functional. Continuous machines are naturally formulated in type theories and we have formalized our findings in Coq. Continuous machines, their equivalence to the standard encoding and correctness of basic operations are now part of Incone, a Coq library for computable analysis. While the correctness proofs use a classical meta-theory with countable choice, the translations and algorithms that are proven correct are all fully executable. Along the way we formally prove some known results such as existence of a self-modulating moduli of continuity for partial continuous operators on Baire space. To illustrate their versatility we use continuous machines to specify some algorithms that operate on objects that cannot be fully described by finite means, such as real numbers and functions. We present particularly simple algorithms for finding the multiplicative inverse of a real number and for composition of partial continuous operators on Baire space. Some of the simplicity is achieved by utilizing the fact that continuous machines are compatible with multivalued semantics. We also connect continuous machines to the construction of precompletions and completions of represented spaces, topics that have recently caught the attention of the computable analysis community.
Roots of Trumpism: Homophily and Social Feedback in Donald Trump Support on Reddit
Venue: arXiv cs.SI 2020
Year: 2020

We study the emergence of support for Donald Trump in Reddit's political discussion. With almost 800k subscribers, "r/The_Donald" is one of the largest communities on Reddit, and one of the main hubs for Trump supporters. It was created in 2015, shortly after Donald Trump began his presidential campaign. By using only data from 2012, we predict the likelihood of being a supporter of Donald Trump in 2016, the year of the last US presidential elections. To characterize the behavior of Trump supporters, we draw from three different sociological hypotheses: homophily, social influence, and social feedback. We operationalize each hypothesis as a set of features for each user, and train classifiers to predict their participation in r/The_Donald. We find that homophily-based and social feedback-based features are the most predictive signals. Conversely, we do not observe a strong impact of social influence mechanisms. We also perform an introspection of the best-performing model to build a "persona" of the typical supporter of Donald Trump on Reddit. We find evidence that the most prominent traits include a predominance of masculine interests, a conservative and libertarian political leaning, and links with politically incorrect and conspiratorial content.
Parametric Phase Tracking via Expectation Propagation
Venue: arXiv cs.IT 2020
Year: 2020

In this work we propose simple algorithms for signal detection in a single-carrier transmission corrupted by a strong phase noise. The proposed phase tracking algorithms are formulated within the framework of a parametric message passing (MP) which reduces the complexity of the Bayesian inference by using distributions from a predefined family; here, of Tikhonov distributions. This stays in line with previous works mainly inspired by the well-known Colavolpe-Barbieri-Caire (CBC) algorithm which gained popularity due to its simplicity and possibility for decoder-aided operation. In our work we mainly focus on practically relevant case of one-shot phase tracking that does not require decoder's feedback. Applying the principles of the expectation propagation (EP), we notably improve the performance of the phase tracking before the decoder's feedback can be even considered. The EP algorithms can be also integrated in the decoding loop in the spirit of joint decoding and phase tracking.
Learning selection strategies in Buchberger's algorithm
Venue: arXiv cs.LG 2020
Year: 2020

Studying the set of exact solutions of a system of polynomial equations largely depends on a single iterative algorithm, known as Buchberger's algorithm. Optimized versions of this algorithm are crucial for many computer algebra systems (e.g., Mathematica, Maple, Sage). We introduce a new approach to Buchberger's algorithm that uses reinforcement learning agents to perform S-pair selection, a key step in the algorithm. We then study how the difficulty of the problem depends on the choices of domain and distribution of polynomials, about which little is known. Finally, we train a policy model using proximal policy optimization (PPO) to learn S-pair selection strategies for random systems of binomial equations. In certain domains, the trained model outperforms state-of-the-art selection heuristics in total number of polynomial additions performed, which provides a proof-of-concept that recent developments in machine learning have the potential to improve performance of algorithms in symbolic computation.
Regret Bounds for Safe Gaussian Process Bandit Optimization
Venue: arXiv cs.LG 2020
Year: 2020

Many applications require a learner to make sequential decisions given uncertainty regarding both the system's payoff function and safety constraints. In safety-critical systems, it is paramount that the learner's actions do not violate the safety constraints at any stage of the learning process. In this paper, we study a stochastic bandit optimization problem where the unknown payoff and constraint functions are sampled from Gaussian Processes (GPs) first considered in [Srinivas et al., 2010]. We develop a safe variant of GP-UCB called SGP-UCB, with necessary modifications to respect safety constraints at every round. The algorithm has two distinct phases. The first phase seeks to estimate the set of safe actions in the decision set, while the second phase follows the GP-UCB decision rule. Our main contribution is to derive the first sub-linear regret bounds for this problem. We numerically compare SGP-UCB against existing safe Bayesian GP optimization algorithms.
Small, Sparse, but Substantial: Techniques for Segmenting Small Agricultural Fields Using Sparse Ground Data
Venue: arXiv cs.CV 2020
Year: 2020

The recent thrust on digital agriculture (DA) has renewed significant research interest in the automated delineation of agricultural fields. Most prior work addressing this problem have focused on detecting medium to large fields, while there is strong evidence that around 40\% of the fields world-wide and 70% of the fields in Asia and Africa are small. The lack of adequate labeled images for small fields, huge variations in their color, texture, and shape, and faint boundary lines separating them make it difficult to develop an end-to-end learning model for detecting such fields. Hence, in this paper, we present a multi-stage approach that uses a combination of machine learning and image processing techniques. In the first stage, we leverage state-of-the-art edge detection algorithms such as holistically-nested edge detection (HED) to extract first-level contours and polygons. In the second stage, we propose image-processing techniques to identify polygons that are non-fields, over-segmentations, or noise and eliminate them. The next stage tackles under-segmentations using a combination of a novel ``cut-point'' based technique and localized second-level edge detection to obtain individual parcels. Since a few small, non-cropped but vegetated or constructed pockets can be interspersed in areas that are predominantly croplands, in the final stage, we train a classifier for identifying each parcel from the previous stage as an agricultural field or not. In an evaluation using high-resolution imagery, we show that our approach has a high F-Score of 0.84 in areas with large fields and reasonable accuracy with an F-Score of 0.73 in areas with small fields, which is encouraging.
One-step regression and classification with crosspoint resistive memory arrays
Venue: arXiv cs.LG 2020
Year: 2020

Machine learning has been getting a large attention in the recent years, as a tool to process big data generated by ubiquitous sensors in our daily life. High speed, low energy computing machines are in demand to enable real-time artificial intelligence at the edge, i.e., without the support of a remote frame server in the cloud. Such requirements challenge the complementary metal-oxide-semiconductor (CMOS) technology, which is limited by the Moore's law approaching its end and the communication bottleneck in conventional computing architecture. Novel computing concepts, architectures and devices are thus strongly needed to accelerate data-intensive applications. Here we show a crosspoint resistive memory circuit with feedback configuration can execute linear regression and logistic regression in just one step by computing the pseudoinverse matrix of the data within the memory. The most elementary learning operation, that is the regression of a sequence of data and the classification of a set of data, can thus be executed in one single computational step by the novel technology. One-step learning is further supported by simulations of the prediction of the cost of a house in Boston and the training of a 2-layer neural network for MNIST digit recognition. The results are all obtained in one computational step, thanks to the physical, parallel, and analog computing within the crosspoint array.
Self-organizing Pattern in Multilayer Network for Words and Syllables
Venue: arXiv cs.CL 2020
Year: 2020

One of the ultimate goals for linguists is to find universal properties in human languages. Although words are generally considered as representing arbitrary mapping between linguistic forms and meanings, we propose a new universal law that highlights the equally important role of syllables, which is complementary to Zipf's. By plotting rank-rank frequency distribution of word and syllable for English and Chinese corpora, visible lines appear and can be fit to a master curve. We discover the multi-layer network for words and syllables based on this analysis exhibits the feature of self-organization which relies heavily on the inclusion of syllables and their connections. Analytic form for the scaling structure is derived and used to quantify how Internet slang becomes fashionable, which demonstrates its usefulness as a new tool to evolutionary linguistics.
Stealing Links from Graph Neural Networks
Venue: arXiv cs.CR 2020
Year: 2020

Graph data, such as chemical networks and social networks, may be deemed confidential/private because the data owner often spends lots of resources collecting the data or the data contains sensitive information, e.g., social relationships. Recently, neural networks were extended to graph data, which are known as graph neural networks (GNNs). Due to their superior performance, GNNs have many applications, such as healthcare analytics, recommender systems, and fraud detection. In this work, we propose the first attacks to steal a graph from the outputs of a GNN model that is trained on the graph. Specifically, given a black-box access to a GNN model, our attacks can infer whether there exists a link between any pair of nodes in the graph used to train the model. We call our attacks link stealing attacks. We propose a threat model to systematically characterize an adversary's background knowledge along three dimensions which in total leads to a comprehensive taxonomy of 8 different link stealing attacks. We propose multiple novel methods to realize these 8 attacks. Extensive experiments on 8 real-world datasets show that our attacks are effective at stealing links, e.g., AUC (area under the ROC curve) is above 0.95 in multiple cases. Our results indicate that the outputs of a GNN model reveal rich information about the structure of the graph used to train the model.
3D Object Detection Method Based on YOLO and K-Means for Image and Point Clouds
Venue: arXiv cs.CV 2020
Year: 2020

Lidar based 3D object detection and classification tasks are essential for autonomous driving(AD). A lidar sensor can provide the 3D point cloud data reconstruction of the surrounding environment. However, real time detection in 3D point clouds still needs a strong algorithmic. This paper proposes a 3D object detection method based on point cloud and image which consists of there parts.(1)Lidar-camera calibration and undistorted image transformation. (2)YOLO-based detection and PointCloud extraction, (3)K-means based point cloud segmentation and detection experiment test and evaluation in depth image. In our research, camera can capture the image to make the Real-time 2D object detection by using YOLO, we transfer the bounding box to node whose function is making 3d object detection on point cloud data from Lidar. By comparing whether 2D coordinate transferred from the 3D point is in the object bounding box or not can achieve High-speed 3D object recognition function in GPU. The accuracy and precision get imporved after k-means clustering in point cloud. The speed of our detection method is a advantage faster than PointNet.
Vision-based techniques for gait recognition
Venue: arXiv cs.CV 2020
Year: 2020

Global security concerns have raised a proliferation of video surveillance devices. Intelligent surveillance systems seek to discover possible threats automatically and raise alerts. Being able to identify the surveyed object can help determine its threat level. The current generation of devices provide digital video data to be analysed for time varying features to assist in the identification process. Commonly, people queue up to access a facility and approach a video camera in full frontal view. In this environment, a variety of biometrics are available - for example, gait which includes temporal features like stride period. Gait can be measured unobtrusively at a distance. The video data will also include face features, which are short-range biometrics. In this way, one can combine biometrics naturally using one set of data. In this paper we survey current techniques of gait recognition and modelling with the environment in which the research was conducted. We also discuss in detail the issues arising from deriving gait data, such as perspective and occlusion effects, together with the associated computer vision challenges of reliable tracking of human movement. Then, after highlighting these issues and challenges related to gait processing, we proceed to discuss the frameworks combining gait with other biometrics. We then provide motivations for a novel paradigm in biometrics-based human recognition, i.e. the use of the fronto-normal view of gait as a far-range biometrics combined with biometrics operating at a near distance.
MatriVasha: A Multipurpose Comprehensive Database for Bangla Handwritten Compound Characters
Venue: arXiv cs.CV 2020
Year: 2020

At present, recognition of the Bangla handwriting compound character has been an essential issue for many years. In recent years there have been application-based researches in machine learning, and deep learning, which is gained interest, and most notably is handwriting recognition because it has a tremendous application such as Bangla OCR. MatrriVasha, the project which can recognize Bangla, handwritten several compound characters. Currently, compound character recognition is an important topic due to its variant application, and helps to create old forms, and information digitization with reliability. But unfortunately, there is a lack of a comprehensive dataset that can categorize all types of Bangla compound characters. MatrriVasha is an attempt to align compound character, and it's challenging because each person has a unique style of writing shapes. After all, MatrriVasha has proposed a dataset that intends to recognize Bangla 120(one hundred twenty) compound characters that consist of 2552(two thousand five hundred fifty-two) isolated handwritten characters written unique writers which were collected from within Bangladesh. This dataset faced problems in terms of the district, age, and gender-based written related research because the samples were collected that includes a verity of the district, age group, and the equal number of males, and females. As of now, our proposed dataset is so far the most extensive dataset for Bangla compound characters. It is intended to frame the acknowledgment technique for handwritten Bangla compound character. In the future, this dataset will be made publicly available to help to widen the research.
On Reachable Assignments in Cycles and Cliques
Venue: arXiv cs.MA 2020
Year: 2020

The efficient and fair distribution of indivisible resources among agents is a common problem in the field of \emph{Multi-Agent-Systems}. We consider a graph-based version of this problem called Reachable Assignments, introduced by Gourves, Lesca, and Wilczynski [AAAI, 2017]. The input for this problem consists of a set of agents, a set of objects, the agent's preferences over the objects, a graph with the agents as vertices and edges encoding which agents can trade resources with each other, and an initial and a target distribution of the objects, where each agent owns exactly one object in each distribution. The question is then whether the target distribution is reachable via a sequence of rational trades. A trade is rational when the two participating agents are neighbors in the graph and both obtain an object they prefer over the object they previously held. We show that Reachable Assignments is NP-hard even when restricting the input graph to be a clique and develop an $O(n^3)$-time algorithm for the case where the input graph is a cycle with $n$ vertices.
Guided Link-Traversal-Based Query Processing
Venue: arXiv cs.DB 2020
Year: 2020

Link-Traversal-Based Query Processing (LTBQP) is a technique for evaluating queries over a web of data by starting with a set of seed documents that is dynamically expanded through following hyperlinks. Compared to query evaluation over a static set of sources, LTBQP is significantly slower because of the number of needed network requests. Furthermore, there are concerns regarding relevance and trustworthiness of results, given that sources are selected dynamically. To address both issues, we propose guided LTBQP, a technique in which information about document linking structure and content policies is passed to a query processor. Thereby, the processor can prune the search tree of documents by only following relevant links, and restrict the result set to desired results by limiting which documents are considered for what kinds of content. In this exploratory paper, we describe the technique at a high level and sketch some of its applications. We argue that such guidance can make LTBQP a valuable query strategy in decentralized environments, where data is spread across documents with varying levels of user trust.
A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov-Smirnov bounds
Venue: arXiv cs.LG 2020
Year: 2020

A new robust algorithm based of the explanation method SurvLIME called SurvLIME-KS is proposed for explaining machine learning survival models. The algorithm is developed to ensure robustness to cases of a small amount of training data or outliers of survival data. The first idea behind SurvLIME-KS is to apply the Cox proportional hazards model to approximate the black-box survival model at the local area around a test example due to the linear relationship of covariates in the model. The second idea is to incorporate the well-known Kolmogorov-Smirnov bounds for constructing sets of predicted cumulative hazard functions. As a result, the robust maximin strategy is used, which aims to minimize the average distance between cumulative hazard functions of the explained black-box model and of the approximating Cox model, and to maximize the distance over all cumulative hazard functions in the interval produced by the Kolmogorov-Smirnov bounds. The maximin optimization problem is reduced to the quadratic program. Various numerical experiments with synthetic and real datasets demonstrate the SurvLIME-KS efficiency.